Below are descriptive and correlational statistics with data from to the International Civic and Citizenship Education Study (ICCS) 2009 and 2016, in which six (Chile, Mexico, Colombia, Paraguay, Guatemala, and the Dominican Republic) and five (Chile, Mexico, Colombia, Peru, and the Dominican Republic) Latin American countries participated, respectively. The following sections are presented: change in support for dictatorship, support for Dictatorship on civic knowledge and support for Dictatorship on institutional trust

Data Frames

ICCS 2009: Chile, Mexico, Colombia, Paraguay, Guatemala, and the Dominican Republic
ICCS 2016: Chile, Mexico, Colombia, Peru, and the Dominican Republic

Sample

All observations

#Data frames with all variables
#2009
dict09 = iccs09 %>%
  mutate(time          = 2009) %>%
  mutate(country       = COUNTRY) %>%
  mutate(idcountry     = IDCNTRY) %>%
  mutate(idschool       = IDSCHOOL) %>%
  mutate(idstudent     = IDSTUD) %>%
  mutate(idstudent = (time*1000000000000 + idcountry*100000000 + idstudent)) %>% #NEW IDSTUDENT
  mutate(dict_safety   = 5-LS2P03D) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ORDER AND SAFETY
  mutate(dict_benefits = 5-LS2P03E) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ECONOMIC BENEFITS
  mutate(dict          = (dict_safety + dict_benefits)/2) %>% #MEAN DIC
  mutate(pv1civ        = PV1CIV) %>%
  mutate(pv2civ        = PV2CIV) %>%
  mutate(pv3civ        = PV3CIV) %>%
  mutate(pv4civ        = PV4CIV) %>%
  mutate(pv5civ        = PV5CIV) %>%
  mutate(civic_knowledge = (pv1civ + pv2civ + pv3civ + pv4civ + pv5civ)/5) %>% #MEAN CIVIC KNOWLEDGE 
  mutate(s_intrust       = INTRUST) %>% #TRUST IN CIVIC INSTITUTIONS 
  mutate(s_opdisc        = OPDISC) %>%  #OPENNESS IN CLASS DISCUSSION
  mutate(s_hisced        = HISCED) %>%  #HIGHEST PARENTAL EDUCATIONAL LEVEL 
  mutate(univ            = ifelse(s_hisced>3, 1, 0)) %>% #UNIVERSITARY PARENTS
  mutate(s_hisei         = HISEI) %>%   #PARENT'S HIGHEST OCCUPATIONAL STATUS
  mutate(s_homelit       = HOMELIT) %>% #HOME LITERACY
  mutate(s_gender        = SGENDER) %>% #GENDER OF STUDENT
  mutate(s_citcon        = CITCON) %>%  #CONVENTIONAL CITIZENSHIP
  mutate(s_citsoc        = CITSOC) %>%  #SOCIAL MOVEMENT REL. CITIZENSHIP
  mutate(s_citeff        = CITEFF) %>%  #CITIZENSHIP SELF-EFFICACY 
  mutate(s_cntatt        = ATTCNT) %>%  #ATTITUDES TOWARDS OWN COUNTRY 
  mutate(s_geneql        = GENEQL) %>%  #ATTITUDES TOWARDS GENDER EQUALITY
  mutate(s_ethrght       = ETHRGHT) %>% #EQUAL RIGHTS FOR ALL ETHNIC GROUPS 
  mutate(l_attviol       = ATTVIOL) %>% #ATTITUDES: USE OF VIOLENCE 
  mutate(l_attdiv        = ATTDIFF) %>% #ATTITUDES: NEIGHBOURHOOD DIVERSITY
  mutate(l_autgov        = AUTGOV) %>%  #AUTHORITARIANISM IN GOVERNMENT 
  mutate(l_attcorr       = ATTCORR) %>% #CORRUPT PRACTICES IN GOVERNMENT
  mutate(l_dislaw        = DISLAW) %>%  #ATTITUDES: DISOBEYING THE LAW
  mutate(l_empclas       = EMPATH) %>%  #EMPATHY TOWARDS CLASSMATES
  mutate(s_poldisc       = POLDISC) %>% #DISCUSSION OF POL. AND SOC. ISSUES
  #TRUST
  mutate(nac_gob         = IS2P27A) %>% #TRUST INSTITUTIONS-NATIONAL GOVERNMENT  
  mutate(local_gob     = IS2P27B) %>% #TRUST INSTITUTIONS-LOCAL GOVERNMENT 
  mutate(courts        = IS2P27C) %>% #TRUST INSTITUTIONS-COURTS
  mutate(police        = IS2P27D) %>% #TRUST INSTITUTIONS-POLICE 
  mutate(pol_parties   = IS2P27E) %>% #TRUST INSTITUTIONS-POLITICAL PARTIES
  mutate(parliament    = IS2P27F) %>% #TRUST INSTITUTIONS-PARLIAMENT
  mutate(media         = IS2P27G) %>% #TRUST INSTITUTIONS-MEDIA
  mutate(ffaa          = IS2P27H) %>% #TRUST INSTITUTIONS-FFAA 
  mutate(school        = IS2P27I) %>% #TRUST INSTITUTIONS-SCHOOL
  mutate(unit_nations  = IS2P27J) %>% #TRUST INSTITUTIONS-UNITED NATIONS 
  mutate(people        = IS2P27K) %>% #TRUST INSTITUTIONS-PEOPLE
  #WEITHINGS
  mutate(totwgts       = TOTWGTS) %>%   #FINAL STUDENT WEIGHT
  mutate(wgtfac1       = WGTFAC1) %>%   #SCHOOL BASE WEIGHT
  mutate(wgtadj1s      = WGTADJ1S) %>%  #SCHOOL WEIGHT ADJUSTMENT-STUDENT STUDY
  mutate(wgtfac2s      = WGTFAC2S) %>%  #CLASS WEIGHT FACTOR
  mutate(wgtadj2s      = WGTADJ2S) %>%  #CLASS WEIGHT ADJUSTMENT
  mutate(wgtadj3s      = WGTADJ3S) %>%  #STUDENT WEIGHT ADJUSTMENT
  mutate(jkzones       = JKZONES) %>%   #JACKKNIFE ZONE - STUDENT STUDY
  mutate(jkreps        = JKREPS)  %>%  #JACKKNIFE REPLICATE CODE
  select(411:463)
  
#2016  
dict16 = iccs16 %>%
  mutate(time            = 2016) %>%
  mutate(country         = COUNTRY) %>%
  mutate(idcountry       = IDCNTRY) %>%
  mutate(idschool        = IDSCHOOL) %>%
  mutate(idstudent       = IDSTUD) %>%
  mutate(idstudent       = (time*100000000000 + idcountry*100000000 + idstudent)) %>% #NEW IDSTUDENT
  mutate(dict_safety     = 5-LS3G02D) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ORDER AND SAFETY
  mutate(dict_benefits   = 5-LS3G02E) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ECONOMIC BENEFITS
  mutate(dict            = (dict_safety + dict_benefits)/2) %>% #MEAN DIC
  mutate(pv1civ          = PV1CIV) %>%
  mutate(pv2civ          = PV2CIV) %>%
  mutate(pv3civ          = PV3CIV) %>%
  mutate(pv4civ          = PV4CIV) %>%
  mutate(pv5civ          = PV5CIV) %>%
  mutate(civic_knowledge = (pv1civ + pv2civ + pv3civ + pv4civ + pv5civ)/5) %>% #MEAN CIVIC KNOWLEDGE 
  mutate(s_intrust       = S_INTRUST) %>% #TRUST IN CIVIC INSTITUTIONS 
  mutate(s_opdisc        = S_OPDISC) %>%  #OPENNESS IN CLASS DISCUSSION
  mutate(s_hisced        = S_HISCED) %>%  #HIGHEST PARENTAL EDUCATIONAL LEVEL 
  mutate(univ            = ifelse(s_hisced>3, 1, 0)) %>% #UNIVERSITARY PARENTS
  mutate(s_hisei         = S_HISEI) %>%   #PARENT'S HIGHEST OCCUPATIONAL STATUS
  mutate(s_homelit       = S_HOMLIT) %>%  #HOME LITERACY
  mutate(s_gender        = S_GENDER) %>%  #GENDER OF STUDENT
  mutate(s_citcon        = S_CITCON) %>%  #CONVENTIONAL CITIZENSHIP
  mutate(s_citsoc        = S_CITSOC) %>%  #SOCIAL MOVEMENT REL. CITIZENSHIP
  mutate(s_citeff        = S_CITEFF) %>%  #CITIZENSHIP SELF-EFFICACY 
  mutate(s_cntatt        = S_CNTATT) %>%  #ATTITUDES TOWARDS OWN COUNTRY 
  mutate(s_geneql        = S_GENEQL) %>%  #ATTITUDES TOWARDS GENDER EQUALITY
  mutate(s_ethrght       = S_ETHRGHT) %>% #EQUAL RIGHTS FOR ALL ETHNIC GROUPS 
  mutate(l_attviol       = L_ATTVIOL) %>% #ATTITUDES: USE OF VIOLENCE 
  mutate(l_attdiv        = L_ATTDIV) %>%  #ATTITUDES: NEIGHBOURHOOD DIVERSITY
  mutate(l_autgov        = L_AUTGOV) %>%  #AUTHORITARIANISM IN GOVERNMENT 
  mutate(l_attcorr       = L_ATTCORR) %>% #CORRUPT PRACTICES IN GOVERNMENT
  mutate(l_dislaw        = L_DISLAW) %>%  #ATTITUDES: DISOBEYING THE LAW
  mutate(l_empclas       = L_EMPCLAS) %>% #EMPATHY TOWARDS CLASSMATES
  mutate(s_poldisc       = S_POLDISC) %>% #DISCUSSION OF POL. AND SOC. ISSUES  
  #TRUST
  mutate(nac_gob         = IS3G26A) %>%   #TRUST INSTITUTIONS-NATIONAL GOVERNMENT 
  mutate(local_gob       = IS3G26B) %>%   #TRUST INSTITUTIONS-LOCAL GOVERNMENT 
  mutate(courts          = IS3G26C) %>%   #TRUST INSTITUTIONS-COURTS
  mutate(police          = IS3G26D) %>%   #TRUST INSTITUTIONS-POLICE
  mutate(pol_parties     = IS3G26E) %>%   #TRUST INSTITUTIONS-POLITICAL PARTIES
  mutate(parliament      = IS3G26F) %>%   #TRUST INSTITUTIONS-PARLIAMENT
  mutate(media           = IS3G26G) %>%   #TRUST INSTITUTIONS-MEDIA
  mutate(ffaa            = IS3G26I) %>%   #TRUST INSTITUTIONS-FFAA
  mutate(school          = IS3G26J) %>%   #TRUST INSTITUTIONS-SCHOOL
  mutate(unit_nations    = IS3G26K) %>%   #TRUST INSTITUTIONS-UNITED NATIONS
  mutate(people          = IS3G26L) %>%   #TRUST INSTITUTIONS-PEOPLE
  #WEITHINGS
  mutate(totwgts         = TOTWGTS) %>%   #FINAL STUDENT WEIGHT
  mutate(wgtfac1         = WGTFAC1) %>%   #SCHOOL BASE WEIGHT
  mutate(wgtadj1s        = WGTADJ1S) %>%  #SCHOOL WEIGHT ADJUSTMENT-STUDENT STUDY
  mutate(wgtfac2s        = WGTFAC2S) %>%  #CLASS WEIGHT FACTOR
  mutate(wgtadj2s        = WGTADJ2S) %>%  #CLASS WEIGHT ADJUSTMENT
  mutate(wgtadj3s        = WGTADJ3S) %>%  #STUDENT WEIGHT ADJUSTMENT
  mutate(jkzones         = JKZONES)  %>%  #JACKKNIFE ZONE - STUDENT STUDY
  mutate(jkreps          = JKREPS)  %>%  #JACKKNIFE REPLICATE CODE
  select(519:571)

#Merge data
dict <- rbind(dict09, dict16)
#All observations with complete row data
dict2 <- dict[complete.cases(dict),]
#Number observations
dict_count <- dict %>% 
  group_by(time, country) %>%
  dplyr::summarise(N=n()) %>%
  mutate(Prop. = N / sum(N))  %>%
  arrange(country)
#Table
kable(dict_count, align = c("cccc"), 
      col.names = c("Year","Country", "Prop.", "N"),
      caption = "Sample with all observations", format="html") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

time	country	N	Prop.
2009	CHL	5173	0.1730332
2016	CHL	5081	0.2006793
2009	COL	6200	0.2073856
2016	COL	5609	0.2215332
2009	DOM	4569	0.1528298
2016	DOM	3937	0.1554959
2009	GTM	3998	0.1337303
2009	MEX	6565	0.2195946
2016	MEX	5526	0.2182551
2016	PER	5166	0.2040365
2009	PRY	3391	0.1134265

Missing Value delete

#Number observations delete missing values
dict_count2 <- dict2 %>% 
  group_by(time, country) %>%
  dplyr::summarise(N=n(), Prop. = (n()/nrow(dict2)))  %>%
  arrange(country)
#Table
kable(dict_count2,  caption = "Sample without missing", format="html",
      col.names = c("Year","Country", "Prop.", "N")) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

time	country	N	Prop.
2009	CHL	4511	0.1093337
2016	CHL	4070	0.0986451
2009	COL	4790	0.1160959
2016	COL	4461	0.1081219
2009	DOM	2164	0.0524492
2016	DOM	2359	0.0571754
2009	GTM	2930	0.0710148
2009	MEX	5127	0.1242638
2016	MEX	4530	0.1097942
2016	PER	4169	0.1010446
2009	PRY	2148	0.0520614

Change in support for dictatorship

Dictatorships are justified when they bring economic benefits 2016: LS2P03E - LS3G02E

#Dictatorships are justified when they bring economic benefits 2016: LS2P03E - LS3G02E
bar_civic <- ggplot(subset(dict, !is.na(dict_benefits)), aes(as.factor(dict_benefits), group = country)) +  
  geom_bar(aes(y = ..prop.., group = country)) +
  geom_text(aes(y= ..prop.. , label = scales::percent(..prop..)), stat= "count", vjust = -0.5) +
  scale_y_continuous(labels = scales::percent, limits = c(0,1)) +
  facet_grid(country ~ time) + 
  ggtitle("Dictatorship are justified when bring economic benefits") +
  scale_x_discrete(labels = c("Very Disagree", "Disagree", "Agree", "Very Agree")) + 
  labs(x = "Agreement", y = "%") 
bar_civic

bar_civic <- ggplot(dict, aes(as.factor(dict_benefits), group = country)) +  
  geom_bar(aes(y = ..prop.., group = country), na.rm = TRUE) +
  geom_text(aes(y= ..prop.. , label = scales::percent(..prop..)), stat= "count", vjust = -0.5) +
  scale_y_continuous(labels = scales::percent, limits = c(0,1)) +
  facet_grid(country ~ time) + 
  ggtitle("Dictatorship are justified when bring economic benefits (include missing)") +
  scale_x_discrete(labels = c("Very Disagree", "Disagree", "Agree", "Very Agree")) + 
  labs(x = "Agreement", y = "%") 
bar_civic

Dictatorships are justified when they bring order and safety 2009: LS2P03D - LS3G02D

#Dictatorships are justified when they bring order and safety 2009: LS2P03D - LS3G02D
bar_civic <- ggplot(subset(dict, !is.na(dict_safety)), aes(as.factor(dict_safety), group = country)) +  
  geom_bar(aes(y = ..prop.., group = country)) +
  geom_text(aes(y= ..prop.. , label = scales::percent(..prop..)), stat= "count", vjust = -0.5) +
  scale_y_continuous(labels = scales::percent, limits = c(0,1)) +
  facet_grid(country ~ time) + 
  ggtitle("Dictatorship are justified when bring order and security") +
  scale_x_discrete(labels = c("Very Disagree", "Disagree", "Agree", "Very Agree")) + 
  labs(x = "Agreement", y = "%") 
bar_civic

bar_civic <- ggplot(dict, aes(as.factor(dict_safety), group = country)) +  
  geom_bar(aes(y = ..prop.., group = country), na.rm = TRUE) +
  geom_text(aes(y= ..prop.. , label = scales::percent(..prop..)), stat= "count", vjust = -0.5) +
  scale_y_continuous(labels = scales::percent, limits = c(0,1)) +
  facet_grid(country ~ time) + 
  ggtitle("Dictatorship are justified when bring order and security (include missing)") +
  scale_x_discrete(labels = c("Very Disagree", "Disagree", "Agree", "Very Agree")) + 
  labs(x = "Agreement", y = "%") 
bar_civic

Support for Dictatorship and civic knowledge

Civic knowledge proxy: Mean to PV1CIV, PV2CIV, PV3CIV, PV4CIV, PV5CIV.

#Proxy Civic Knowledge
civic_country_mean09 <- dict %>% filter(time==2009)  %>% group_by(country) %>%
  dplyr::summarise(mean=mean(civic_knowledge),sd=sd(civic_knowledge),n=n()) %>%
  dplyr::mutate(se=sd/sqrt(n)) %>%
  mutate (time=2009)

civic_country_mean16 <- dict %>% filter(time==2016)  %>% group_by(country) %>%  
  dplyr::summarise(mean=mean(civic_knowledge),sd=sd(civic_knowledge),n=n()) %>%
  dplyr::mutate(se=sd/sqrt(n)) %>%
  mutate (time=2016) 

civic_country_mean <- merge(civic_country_mean09, civic_country_mean16, all = T)

kable(civic_country_mean, align = c("cccccc"), caption = "Mean Civic Knowledge") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

country	mean	sd	n	se	time
CHL	490.2998	91.60244	5081	1.2850867	2016
CHL	494.0536	85.69706	5173	1.1915017	2009
COL	465.8547	77.26525	6200	0.9812696	2009
COL	486.0054	79.18727	5609	1.0573351	2016
DOM	380.9117	61.77524	4569	0.9139109	2009
DOM	382.8483	75.71249	3937	1.2066598	2016
GTM	432.6359	71.50737	3998	1.1309136	2009
MEX	455.7181	78.47208	6565	0.9684959	2009
MEX	469.3019	79.25553	5526	1.0661643	2016
PER	442.6917	87.93071	5166	1.2233856	2016
PRY	428.7423	84.05566	3391	1.4434543	2009

#Graphs Bar
#All
civic_know <- ggplot(civic_country_mean, aes(reorder(to_label(country), -mean),mean), group = time)
civic_know <- civic_know +  geom_bar(stat = "summary") +
  facet_grid(~ time) + 
  geom_text(aes(label = format(mean, digits = 2), vjust=0.3, hjust=-0.2), size=2, colour="black") +
  coord_flip() +
  theme(axis.text=element_text(size=5,face="bold"),
        axis.title=element_text(size=10,face="bold")) + 
  labs(x="Country", y="Civic Knowledge")
civic_know

#Boxplot 
box_civic = ggplot(dict, aes(reorder(to_label(country), -civic_knowledge), civic_knowledge), group = time) 
box_civic + geom_boxplot() +  
  facet_grid(~ time) + 
  coord_flip() + 
  theme(axis.text=element_text(size=5),
        axis.title=element_text(size=10,face="bold")) + 
  labs(x=" ", y="Civic Knowledge")

Latin American 2009-2016: Dictatorship and civic knowledge

Dictatorships are justified when they bring economic benefits and civic knowledge

#Dictatorships are justified when they bring economic benefits 
scatter <- ggplot(dict, aes(civic_knowledge, dict_benefits, colour = factor(country))) 
    scatter + geom_smooth(method = "lm", alpha = 0.1) + 
      facet_wrap(~ time) +
    labs(x = "Civic knowledge", y = "Support to dictatorship (economic benefits)", colour = "country")

Dictatorships are justified when they bring order and safety and civic knowledge

#Dictatorships are justified when they bring order and safety 
scatter <- ggplot(dict, aes(civic_knowledge, dict_safety, colour = factor(country))) 
    scatter + geom_smooth(method = "lm", alpha = 0.1) + 
      facet_wrap(~ time) +
    labs(x = "Civic knowledge", y = "Support to dictatorship (order and safety)", colour = "country")

Dictatorships are justified (means of the previous two) and civic knowledge

#Dictatorships are justified mean 
scatter <- ggplot(dict, aes(civic_knowledge, dict, colour = factor(country))) 
    scatter + geom_smooth(method = "lm", alpha = 0.1) + 
      facet_wrap(~ time) +
    labs(x = "Civic knowledge", y = "Support to dictatorship (mean)", colour = "country")

Latin American 2009-2016: institutional trust and civic knowledge

#Trust in civic institutions and civic knowledge
scatter <- ggplot(dict, aes(civic_knowledge, s_intrust, colour = factor(country))) 
    scatter + geom_smooth(method = "lm", alpha = 0.1) + 
      facet_wrap(~ time) +
    labs(x = "Civic knowledge", y = "Trust in civic institutions", colour = "country")

Support for Dictatorship and institutional trust

Latin American 2009-2016: Institutional trust

in_trust09 <- dict2 %>% filter(time==2009)  %>% group_by(country) %>%
  dplyr::summarise(mean=mean(s_intrust),sd=sd(s_intrust),n=n()) %>%
  dplyr::mutate(se=sd/sqrt(n)) %>%
  mutate (time=2009)

in_trust16 <- dict2 %>% filter(time==2016)  %>% group_by(country) %>%  
  dplyr::summarise(mean=mean(s_intrust),sd=sd(s_intrust),n=n()) %>%
  dplyr::mutate(se=sd/sqrt(n)) %>%
  mutate (time=2016) 

in_trust_mean <- merge(in_trust09, in_trust16, all = T)

kable(in_trust_mean, align = c("cccccc"), caption = "Mean trust in civic institutions") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

country	mean	sd	n	se	time
CHL	47.06615	10.965115	4070	0.1718763	2016
CHL	49.81702	9.650716	4511	0.1436889	2009
COL	48.06496	9.497960	4461	0.1422048	2016
COL	49.35341	10.385844	4790	0.1500631	2009
DOM	54.14559	11.956145	2164	0.2570174	2009
DOM	54.98023	11.327530	2359	0.2332229	2016
GTM	46.91566	9.569341	2930	0.1767861	2009
MEX	48.46713	10.355470	5127	0.1446233	2009
MEX	49.61967	10.893892	4530	0.1618579	2016
PER	47.84578	9.585716	4169	0.1484597	2016
PRY	49.42522	9.333150	2148	0.2013776	2009

#Graphs Bar
#All
bar_intrust <- ggplot(in_trust_mean, aes(reorder(to_label(country), mean),mean), group = time)
bar_intrust <- bar_intrust +  geom_bar(stat = "summary") +
  facet_grid(~ time) + 
  geom_text(aes(label = format(mean, digits = 2), vjust=0.3, hjust=-0.2), size=2, colour="black") +
  coord_flip() +
  theme(axis.text=element_text(size=5,face="bold"),
        axis.title=element_text(size=10,face="bold")) + 
  labs(x="Country", y="Trust in civic institutions")
bar_intrust

#Boxplot 
box_intrust = ggplot(dict, aes(reorder(to_label(country), -s_intrust), s_intrust), group = time) 
box_intrust + geom_boxplot() +  
  facet_grid(~ time) + 
  coord_flip() + 
  theme(axis.text=element_text(size=5),
        axis.title=element_text(size=10,face="bold")) + 
  labs(x=" ", y="Trust in civic institutions")

Latin American 2009-2016: Dictatorship and institutional trust

Dictatorships are justified when they bring economic benefits and institutional trust

#Dictatorships are justified when they bring economic benefits 
scatter <- ggplot(dict, aes(s_intrust, dict_benefits, colour = factor(country))) 
    scatter + geom_smooth(method = "lm", alpha = 0.1) + 
      facet_wrap(~ time) +
    labs(x = "Trust in civic institutions", y = "Support to dictatorship (economic benefits)", colour = "country")

Dictatorships are justified when they bring order and safety and institutional trust

#Dictatorships are justified when they bring order and safety 
scatter <- ggplot(dict, aes(s_intrust, dict_safety, colour = factor(country))) 
    scatter + geom_smooth(method = "lm", alpha = 0.1) + 
      facet_wrap(~ time) +
    labs(x = "Trust in civic institutions", y = "Support to dictatorship (order and safety)", colour = "country")

Dictatorships are justified (means of the previous two) and institutional trust

#Dictatorships are justified mean 
scatter <- ggplot(dict, aes(s_intrust, dict, colour = factor(country))) 
    scatter + geom_smooth(method = "lm", alpha = 0.1) + 
      facet_wrap(~ time) +
    labs(x = "Trust in civic institutions", y = "Support to dictatorship (mean)", colour = "country")

Regression Model by country (simple)

#Models Chile
ch09= dict %>% filter(country=="CHL" & time==2009) 
mch09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=ch09)
ch16= dict %>% filter(country=="CHL" & time==2016) 
mch16 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=ch16)

#Models Colombia
col09= dict %>% filter(country=="COL" & time==2009) 
mcol09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=col09)
col16= dict %>% filter(country=="COL" & time==2016) 
mcol16 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=col16)

#Models República Dominicana
dom09= dict %>% filter(country=="DOM" & time==2009) 
mdom09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=dom09)
dom16= dict %>% filter(country=="DOM" & time==2016) 
mdom16 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=dom16)

#Models México
mex09= dict %>% filter(country=="MEX" & time==2009) 
mmex09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=mex09)
mex16= dict %>% filter(country=="MEX" & time==2016) 
mmex16 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=mex16)

#Models Guatemala 2009
gtm09= dict %>% filter(country=="GTM" & time==2009) 
mgtm09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=gtm09)

#Models Paraguay 2009
pry09= dict %>% filter(country=="PRY" & time==2009) 
mpry09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=pry09)

#Models Perú 2016
per16= dict %>% filter(country=="PER" & time==2016) 
mper16 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=per16)

#Table
library(stargazer)
stargazer(l=list(mch09,mch16,mcol09,mcol16,mdom09, mdom16,mmex09, mmex16), digits = 3, type="html", 
          column.labels=c("Chile 2009", "Chile 2016", "Colombia 2009", "Colombia 2016", "Rep. Dom. 2009", "Rep. Dom. 2016", "Mexico 2009", "Mexico 2016"), 
          covariate.labels =c("Trust institutions", "Civic Knowledge"), 
          dep.var.labels = "Support dictatorship")


	Dependent variable:

	Support dictatorship
	Chile 2009	Chile 2016	Colombia 2009	Colombia 2016	Rep. Dom. 2009	Rep. Dom. 2016	Mexico 2009	Mexico 2016
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)

Trust institutions	0.014^***	0.013^***	0.009^***	0.011^***	0.011^***	0.012^***	0.010^***	0.016^***
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)

Civic Knowledge	-0.003^***	-0.002^***	-0.002^***	-0.002^***	-0.002^***	-0.003^***	-0.002^***	-0.002^***
	(0.0001)	(0.0002)	(0.0001)	(0.0001)	(0.0002)	(0.0002)	(0.0001)	(0.0001)

Constant	3.330^***	3.059^***	3.231^***	3.179^***	3.228^***	3.626^***	3.184^***	2.819^***
	(0.094)	(0.095)	(0.078)	(0.088)	(0.124)	(0.118)	(0.080)	(0.091)


Observations	5,059	4,909	5,733	5,325	3,450	3,395	6,129	5,289
Log Likelihood	-5,915.167	-6,382.484	-5,899.622	-5,766.078	-4,168.101	-4,122.095	-6,661.336	-6,419.753
Akaike Inf. Crit.	11,840.330	12,774.970	11,809.240	11,542.160	8,346.201	8,254.190	13,332.670	12,849.510
Bayesian Inf. Crit.	11,872.980	12,807.460	11,842.510	11,575.060	8,376.932	8,284.840	13,366.270	12,882.370

Note:	p<0.1; p<0.05; p<0.01

stargazer(l=list(mgtm09, mpry09, mper16), digits = 3, type="html", 
          column.labels=c("Guatemala 2009", "Paraguay 2016", "Perú 2009"), 
          covariate.labels =c("Trust institutions", "Civic Knowledge"), 
          dep.var.labels = "Support dictatorship")


	Dependent variable:

	Support dictatorship
	Guatemala 2009	Paraguay 2016	Perú 2009
	(1)	(2)	(3)

Trust institutions	0.009^***	0.008^***	0.010^***
	(0.001)	(0.001)	(0.001)

Civic Knowledge	-0.002^***	-0.003^***	-0.001^***
	(0.0002)	(0.0002)	(0.0001)

Constant	3.320^***	3.486^***	2.814^***
	(0.100)	(0.120)	(0.082)


Observations	3,745	2,880	4,975
Log Likelihood	-3,779.178	-3,237.766	-4,917.179
Akaike Inf. Crit.	7,568.355	6,485.532	9,844.359
Bayesian Inf. Crit.	7,599.496	6,515.360	9,876.919

Note:	p<0.1; p<0.05; p<0.01

Descriptives

Dictatorships are justified when they bring economic benefits

tapply(dict$dict_benefits, dict$country, summary)

$CHL
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   2.534   3.000   4.000     106 

$COL
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1.00    2.00    3.00    2.78    3.00    4.00     250 

$DOM
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   2.875   4.000   4.000     598 

$GTM
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   2.932   3.000   4.000      84 

$MEX
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   2.755   3.000   4.000     270 

$PER
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   2.859   3.000   4.000      65 

$PRY
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   2.731   3.000   4.000     150

Dictatorships are justified when they bring order and security

tapply(dict$dict_safety, dict$country, summary)

$CHL
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   2.557   3.000   4.000     124 

$COL
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1.00    2.00    3.00    2.83    3.00    4.00     288 

$DOM
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   2.935   4.000   4.000     622 

$GTM
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   3.000   3.000   2.943   3.000   4.000      98 

$MEX
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   2.753   3.000   4.000     306 

$PER
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   3.000   3.000   2.924   3.000   4.000      78 

$PRY
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   2.804   3.000   4.000     138

Institutional trust

tapply(dict$s_intrust, dict$country, summary)

$CHL
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  20.21   43.02   47.28   48.48   54.39   77.35     165 

$COL
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  20.21   43.02   47.28   48.89   54.39   77.35     547 

$DOM
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  20.21   47.27   54.36   54.59   62.22   77.35    1346 

$GTM
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  20.21   40.94   45.12   47.17   51.90   77.35     194 

$MEX
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  20.21   40.98   47.28   49.16   54.39   77.35     457 

$PER
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  21.78   40.98   47.28   48.03   54.36   75.73     129 

$PRY
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  20.21   43.02   49.53   49.66   54.39   77.35     423

Civic Knowledge

tapply(dict$civic_knowledge, dict$country, summary)

$CHL
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  206.6   426.8   494.8   492.2   559.4   780.4 

$COL
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  194.1   419.5   478.4   475.4   531.6   707.2 

$DOM
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  141.0   334.5   376.8   381.8   425.6   629.2 

$GTM
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  183.3   385.0   429.8   432.6   482.2   659.0 

$MEX
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  175.1   403.3   463.3   461.9   520.2   715.2 

$PER
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  174.7   378.4   444.7   442.7   508.6   664.6 

$PRY
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  156.8   369.8   423.7   428.7   488.1   711.0

Multilevel Regression Model (simple)

Model to 2009

#Model to 2009
model09 = lmer(dict ~ 1 + s_intrust + civic_knowledge +  (1 | country), weights=totwgts, data=dict09)
stargazer(model09, digits = 3, type="html", 
          covariate.labels =c("Trust institutions", "Civic Knowledge"), 
          dep.var.labels = "Support dictatorship")


	Dependent variable:

	Support dictatorship

Trust institutions	0.010^***
	(0.0004)

Civic Knowledge	-0.002^***
	(0.0001)

Constant	3.197^***
	(0.053)


Observations	26,996
Log Likelihood	-35,291.470
Akaike Inf. Crit.	70,592.940
Bayesian Inf. Crit.	70,633.950

Note:	p<0.1; p<0.05; p<0.01

library(sjPlot)
fig09=plot_model(model09, type = "re", 
           show.legend = FALSE,
           show.values = TRUE,
           facet.grid = FALSE,
           y.offset = .4,
           value.offset = .4,
           value.size = 3.5,
           color="darkgray",
           sort.est= 2,
           title = " ",)
fig09

Model to 2016

#Model to 2016
model16 = lmer(dict ~ 1 + s_intrust + civic_knowledge +  (1 | country), weights=totwgts, data=dict16)
stargazer(model16, digits = 3, type="html", 
          covariate.labels =c("Trust institutions", "Civic Knowledge"), 
          dep.var.labels = "Support dictatorship")


	Dependent variable:

	Support dictatorship

Trust institutions	0.015^***
	(0.0005)

Civic Knowledge	-0.002^***
	(0.0001)

Constant	2.924^***
	(0.069)


Observations	23,893
Log Likelihood	-32,236.060
Akaike Inf. Crit.	64,482.110
Bayesian Inf. Crit.	64,522.520

Note:	p<0.1; p<0.05; p<0.01

fig16=plot_model(model16, type = "re", 
           show.legend = FALSE,
           show.values = TRUE,
           facet.grid = FALSE,
           y.offset = .4,
           value.offset = .4,
           value.size = 3.5,
           color="darkgray",
           sort.est= 2,
           title = " ",)
fig16

Trust in political institutions and support for authoritarianism in Latin American students: Does civic knowledge make a difference?

Juan Carlos Castillo, Daniel Miranda, Catalina Miranda & José Conejeros

07/08/2019